Cargando…

Disease Localization in Multilayer Networks

We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infe...

Descripción completa

Detalles Bibliográficos
Autores principales: de Arruda, Guilherme Ferraz, Cozzo, Emanuele, Peixoto, Tiago P., Rodrigues, Francisco A., Moreno, Yamir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Physical Society 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219475/
http://dx.doi.org/10.1103/PhysRevX.7.011014
_version_ 1783532999126548480
author de Arruda, Guilherme Ferraz
Cozzo, Emanuele
Peixoto, Tiago P.
Rodrigues, Francisco A.
Moreno, Yamir
author_facet de Arruda, Guilherme Ferraz
Cozzo, Emanuele
Peixoto, Tiago P.
Rodrigues, Francisco A.
Moreno, Yamir
author_sort de Arruda, Guilherme Ferraz
collection PubMed
description We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes.
format Online
Article
Text
id pubmed-7219475
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher American Physical Society
record_format MEDLINE/PubMed
spelling pubmed-72194752020-05-13 Disease Localization in Multilayer Networks de Arruda, Guilherme Ferraz Cozzo, Emanuele Peixoto, Tiago P. Rodrigues, Francisco A. Moreno, Yamir Phys Rev X Research Articles We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes. American Physical Society 2017-02-02 2017-01-01 /pmc/articles/PMC7219475/ http://dx.doi.org/10.1103/PhysRevX.7.011014 Text en Published by the American Physical Society http://creativecommons.org/licenses/by/3.0/ Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License (http://creativecommons.org/licenses/by/3.0/) . Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
spellingShingle Research Articles
de Arruda, Guilherme Ferraz
Cozzo, Emanuele
Peixoto, Tiago P.
Rodrigues, Francisco A.
Moreno, Yamir
Disease Localization in Multilayer Networks
title Disease Localization in Multilayer Networks
title_full Disease Localization in Multilayer Networks
title_fullStr Disease Localization in Multilayer Networks
title_full_unstemmed Disease Localization in Multilayer Networks
title_short Disease Localization in Multilayer Networks
title_sort disease localization in multilayer networks
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219475/
http://dx.doi.org/10.1103/PhysRevX.7.011014
work_keys_str_mv AT dearrudaguilhermeferraz diseaselocalizationinmultilayernetworks
AT cozzoemanuele diseaselocalizationinmultilayernetworks
AT peixototiagop diseaselocalizationinmultilayernetworks
AT rodriguesfranciscoa diseaselocalizationinmultilayernetworks
AT morenoyamir diseaselocalizationinmultilayernetworks